AI- Driven Technologies for Water Quality Monitoring : A Comprehensive Review and Future Perspectives

Traditional methods of water quality measurement are typically slow, laborious, and limiting, similar to looking at an entire forest using a magnifying glass. However, with the current advancements in artificial intelligence (AI), the method has been reengineered as a quicker, more precise, and very detailed monitoring system. This research targets some of the AI-based technologies employed in water quality monitoring like Machine learning methods for example, Internet of Things (IoT) sensors combined with smart sensors capable of wishing to support real-time data sampling and edge computing to enable instantaneous analytics, predictive modeling and time series analysis to assist in water quality condition forecasting. Likewise we have Natural language processing (NLP) enables data integration and user interface. Existing issues like data quality, model interpretability, and integration are also referred to in the paper along with directions for future research like explainable AI and multi-source data fusion. Drawing on recent developments, this article provides a combined overview of how AI can help toward sustainable and smart water quality management.